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TNN
2008
82views more  TNN 2008»
14 years 11 months ago
Deterministic Learning for Maximum-Likelihood Estimation Through Neural Networks
In this paper, a general method for the numerical solution of maximum-likelihood estimation (MLE) problems is presented; it adopts the deterministic learning (DL) approach to find ...
Cristiano Cervellera, Danilo Macciò, Marco ...
ISCAS
1994
IEEE
104views Hardware» more  ISCAS 1994»
15 years 3 months ago
Stereo Correspondence with Discrete-Time Cellular Neural Networks
In this paper, we propose a new approach of solving the stereopsis problem with a discrete-time cellular neural network(DTCNN) where each node has connectionsonly with its local n...
Sungjun Park, Seung-Jai Min, Soo-Ik Chae
ISDA
2009
IEEE
15 years 5 months ago
A Fuzzy Wavelet Neural Network Model for System Identification
In this paper, a fuzzy wavelet neural network model is proposed for system identification problems. The proposed model is obtained from the traditional Takagi-Sugeno-Kang (TSK) fu...
Sevcan Yilmaz, Yusuf Oysal
SSPR
1998
Springer
15 years 3 months ago
Modified Minimum Classification Error Learning and Its Application to Neural Networks
A novel method to improve the generalization performance of the Minimum Classification Error (MCE) / Generalized Probabilistic Descent (GPD) learning is proposed. The MCE/GPD learn...
Hiroshi Shimodaira, Jun Rokui, Mitsuru Nakai
GECCO
2010
Springer
181views Optimization» more  GECCO 2010»
15 years 4 months ago
Evolving neural networks in compressed weight space
We propose a new indirect encoding scheme for neural networks in which the weight matrices are represented in the frequency domain by sets of Fourier coefficients. This scheme exp...
Jan Koutnik, Faustino J. Gomez, Jürgen Schmid...